Flexible support for time and costs in scenario-aware dataflow

Hartmanns, Arnd; Hermanns, Holger; Bungert, Michael

doi:10.1145/2968478.2968496

Cited by 9 publications

(8 citation statements)

References 16 publications

(35 reference statements)

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“…It is implemented in C# and works on Windows as well as on Linux and Mac OS via the Mono runtime. Built on common infrastructure in the Modest Toolset, it supports Modest, xSADF [44] and Jani as input languages, and has access to a fast state space exploration engine that compiles models to bytecode. mcsta computes unbounded and reward-bounded reachability probabilities and expected accumulated rewards on MDP and MA, and additionally time-bounded probabilities on MA.…”

Section: Model Checkersmentioning

confidence: 99%

The 2019 Comparison of Tools for the Analysis of Quantitative Formal Models

Hahn

Hartmanns

Hensel

et al. 2019

Lecture Notes in Computer Science

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Quantitative formal models capture probabilistic behaviour, real-time aspects, or general continuous dynamics. A number of tools support their automatic analysis with respect to dependability or performance properties. QComp 2019 is the first, friendly competition among such tools. It focuses on stochastic formalisms from Markov chains to probabilistic timed automata specified in the Jani model exchange format, and on probabilistic reachability, expected-reward, and steady-state properties. QComp draws its benchmarks from the new Quantitative Verification Benchmark Set. Participating tools, which include probabilistic model checkers and planners as well as simulation-based tools, are evaluated in terms of performance, versatility, and usability. In this paper, we report on the challenges in setting up a quantitative verification competition, present the results of QComp 2019, summarise the lessons learned, and provide an outlook on the features of the next edition of QComp.

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Section: Model Checkersmentioning

confidence: 99%

The 2019 Comparison of Tools for the Analysis of Quantitative Formal Models

Hahn

Hartmanns

Hensel

et al. 2019

Lecture Notes in Computer Science

Self Cite

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“…It uses the toolset's infrastructure to transform various input languages into an internal metamodel corresponding to a network of stochastic hybrid automata (SHA [30]) with discrete variables. The following input languages are currently supported: -Modest [30], a process algebra-based modelling language for stochastic timed systems featuring high-level constructs such as recursive process calls, loops, and exception handling; -xSADF [35], an extension of scenario-aware dataflow with continuous probability distributions and nondeterminism, a formalism particularly suited to the study of embedded streaming applications; and -Jani [14], a model exchange format designed to improve the interoperation of quantitative verification tools. Other tools provide converters to Jani from various Petri net formats or the Prism language [47].…”

Section: The Modes Toolmentioning

confidence: 99%

Automated compositional importance splitting

Budde¹,

D’Argenio²,

Hartmanns³

2019

Science of Computer Programming

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In the formal verification of stochastic systems, statistical model checking uses simulation to overcome the state space explosion problem of probabilistic model checking. Yet its runtime explodes when faced with rare events, unless a rare event simulation method like importance splitting is used. The effectiveness of importance splitting hinges on nontrivial model-specific inputs: an importance function with matching splitting thresholds. This prevents its use by nonexperts for general classes of models. In this paper, we present an automated method to derive the importance function. It considers both the structure of the model and of the formula characterising the rare event. It is memory-efficient by exploiting the compositional nature of formal models. We experimentally evaluate it in various combinations with two approaches to threshold selection as well as different splitting techniques for steady-state and transient properties. We find that Restart splitting combined with thresholds determined via a new expected success method most reliably succeeds and performs very well for transient properties. It remains competitive in the steady-state case, which is however challenging to all combinations we consider. All methods are implemented in the modes tool of the Modest Toolset and in the Fig rare event simulator.state space explosion problem limits this approach to small models. For other models, in particular those involving events governed by general continuous probability distributions, model checking techniques only exist for specific subclasses with limited scalability [55] or merely compute probability bounds [31].Statistical model checking (SMC [38,72]), i.e. using Monte Carlo simulation with formal models, has become a popular alternative for large models, and for formalisms not amenable to (traditional) probabilistic model checking like stochastic (timed) automata [9,18]. SMC trades memory for runtime: memory usage is constant, but the number of simulation runs which are needed to converge to a result can easily explode with the desired precision. This is exacerbated in the presence of rare events. For instance, when the true probability of an event is 10 −15 , one may want that the error of an estimation is no larger than 10 −16 . Such tight requirements in the precision of estimations may render traditional Monte Carlo simulation approaches infeasible [24,65].Rare event simulation methods (RES [58]) have been developed to attack this problem. They increase the number of simulation runs that reach the rare event and adjust the statistical evaluation accordingly. Broadly speaking, the main RES methods are importance sampling and importance splitting. They complement each other in several application domains [57]. The former modifies the probability distributions which dictate the stochastic behaviour of the model, with the aim to make the event more likely to occur. The challenge lies in finding a "good" change of measure to modify probabilities in an effective way. Importance splitting instead does n...

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“…Dataflow formalisms are popular in the study of embedded data processing applications. The recently introduced formalism xSADF [35], an extension of scenario-aware dataflow [56], adds cost annotations (to model, for example, power consumption), nondeterminism, and continuous stochastic execution times. It is equipped with a compositional semantics in terms of STA, which is implemented in the Modest Toolset.…”

Section: Modelling Languagesmentioning

confidence: 99%

JANI: Quantitative Model and Tool Interaction

Budde

Dehnert

Hahn

et al. 2017

Tools and Algorithms for the Construction and Analysis of Systems

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The formal analysis of critical systems is supported by a vast space of modelling formalisms and tools. The variety of incompatible formats and tools however poses a significant challenge to practical adoption as well as continued research. In this paper, we propose the Jani model format and tool interaction protocol. The format is a metamodel based on networks of communicating automata and has been designed for ease of implementation without sacrificing readability. The purpose of the protocol is to provide a stable and uniform interface between tools such as model checkers, transformers, and user interfaces. Jani uses the Json data format, inheriting its ease of use and inherent extensibility. Jani initially targets, but is not limited to, quantitative model checking. Several existing tools now support the verification of Jani models, and automatic converters from a diverse set of higher-level modelling languages have been implemented. The ultimate purpose of Jani is to simplify tool development, encourage research cooperation, and pave the way towards a future competition in quantitative model checking.

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Flexible support for time and costs in scenario-aware dataflow

Cited by 9 publications

References 16 publications

The 2019 Comparison of Tools for the Analysis of Quantitative Formal Models

The 2019 Comparison of Tools for the Analysis of Quantitative Formal Models

Automated compositional importance splitting

JANI: Quantitative Model and Tool Interaction

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